Podcasts about Crowdsourcing

Matters Microbial #96: Molecular Microbiology in High Schools June 19, 2025 Today, Bob Kuhn, biotechnology teacher at the Fulton County Schools Innovation Academy STEM high school in Alpharetta, Georgia joins the #QualityQuorum to discuss his strategies for teaching molecular microbiology skills and concepts to high school students — and why that matters. Host: Mark O. Martin Guest: Bob Kuhn Subscribe: Apple Podcasts, Spotify Become a patron of Matters Microbial! Links for this episode An article on Mr. Kuhn's original microbiological interest:  salt marsh foraminifera. Another interest:  a video of the beautiful organism Volvox. An overview of the fantastic world of protists. A classroom CURE that Mr. Kuhn uses:  the Tiny Earth program. A classroom CURE that Mr. Kuhn uses:  the SEAPHAGES program. A classroom CURE that Mr. Kuhn uses:  the Evolving Stem program. A classroom CURE that Mr. Kuhn uses:  the Discover the Microbes Within program. Mr. Kuhn's classroom received help from the University of Georgia Microscopy Center. A video of the Wolbachia Project with which Mr. Kuhn has collaborated for years. An overview of wonderful and various isopods, including the “roly polies” used in some Wolbachia work. The Journal of Emerging Investigators, in which some of Mr. Kuhn's high school students have published. The LTEE program that studies evolution in E. coli.   The Experiment.com program that supported some of Mr. Kuhn's students. The very inspirational MEGAplate video that demonstrates how quickly antibiotic resistance can occur. Innovation Academy STEM program in Georgia, where Mr. Kuhn works with high school students. Intro music is by Reber Clark Send your questions and comments to mattersmicrobial@gmail.com

In this episode, I'm pulling back the curtain on what it really takes to market a design book—especially when you're doing most of it yourself. This episode is all about doing things with intention, working within limits, and inviting you to help shape this journey with me.Leave Me A Message! I need your feedback on the ideas from this episode.  Go here and hit “record!” https://www.speakpipe.com/SlowStyleHome Want to finally define your style? Grab your free worksheet and uncover your personal aesthetic!

Imagine being able to access sensor and satellite imagery, civilian smartphones, and other publicly available information in real time to inform tactical and operational decisions. If that sounds like a good idea, you might not be in the intel community. Could we, or should we, use that data? Here with a legal perspective on that question is Al Broadbent, an attorney at Womble Bond Dickinson.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

LMArena cofounders Anastasios N. Angelopoulos, Wei-Lin Chiang, and Ion Stoica sit down with a16z general partner Anjney Midha to talk about the future of AI evaluation. As benchmarks struggle to keep up with the pace of real-world deployment, LMArena is reframing the problem: what if the best way to test AI models is to put them in front of millions of users and let them vote? The team discusses how Arena evolved from a research side project into a key part of the AI stack, why fresh and subjective data is crucial for reliability, and what it means to build a CI/CD pipeline for large models.They also explore:Why expert-only benchmarks are no longer enough.How user preferences reveal model capabilities — and their limits.What it takes to build personalized leaderboards and evaluation SDKs.Why real-time testing is foundational for mission-critical AI.Follow everyone on X:Anastasios N. AngelopoulosWei-Lin ChiangIon StoicaAnjney MidhaTimestamps0:04 -  LLM evaluation: From consumer chatbots to mission-critical systems6:04 -  Style and substance: Crowdsourcing expertise18:51 -  Building immunity to overfitting and gaming the system29:49 -  The roots of LMArena41:29 -   Proving the value of academic AI research48:28 -  Scaling LMArena and starting a company59:59 -  Benchmarks, evaluations, and the value of ranking LLMs1:12:13 -  The challenges of measuring AI reliability1:17:57 -  Expanding beyond binary rankings as models evolve1:28:07 -  A leaderboard for each prompt1:31:28 -  The LMArena roadmap1:34:29 -  The importance of open source and openness1:43:10 -  Adapting to agents (and other AI evolutions) Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

In this episode of the IoT For All Podcast, Christian Rouffaert, Founder and CEO of Teragence, joins Ryan Chacon to discuss achieving reliable IoT connectivity with crowdsourced data. Specialist IoT connectivity providers rely on the connectivity provided by local operators and do not receive any honest information on the level of coverage, leaving everyone at the mercy of the incumbents. Teragence aims to change change that by providing location-specific, transparent data about every licensed operators' level of coverage.Christian Rouffaert studied engineering in Belgium and acquired an MBA from London Business School. He is the founder and CEO of Teragence. As a jack-of-all trades, Christian has worked across a wide range of domains in the telecoms sector, including data centers, mobile networks, finance, and sales in Europe, Asia, and the United States. Previously, he worked as a management consultant at Accenture.Teragence uses crowdsourced data to deliver actionable network insights in a bite-sized format. Their data is crowdsourced from handsets and IoT devices and is combined with advanced geospatial analytics and machine learning to deliver actionable network insights – information about mobile networks that is useful and practical.Discover more about IoT at https://www.iotforall.comFind IoT solutions: https://marketplace.iotforall.comMore about Teragence: https://teragence.comConnect with Christian: https://www.linkedin.com/in/christian-rouffaert-347993/(00:00) Intro(00:11) Christian Rouffaert and Teragence(01:00) The importance of reliable cellular connectivity(02:29) Challenges of IoT connectivity(04:21) Navigating the IoT ecosystem and value chain(07:55) Convincing customers that their problem is solvable(12:04) Transparency issues with mobile operators(14:10) Alternatives to local operator coverage data(16:31) Real-world success stories(19:39) Gathering coverage data internationally(21:39) The future of connectivity intelligence(23:04) Learn more and follow upSubscribe on YouTube: https://bit.ly/2NlcEwmJoin Our Newsletter: https://newsletter.iotforall.comFollow Us on Social: https://linktr.ee/iot4all

In this thrilling episode of our DND sidequest series, Rise of the Defiler, the crew wage glorious battle on Andor's pirates. It takes three players and one DM to barely match Laurel's chaotic energy!   To hear all of our sidequest series in their full audiophonic glory, sign up for our Patreon at: Patreon.com/ShatteredWorldsRPG   Join our Discord community! Follow this link and get in on the fun: https://discord.gg/tq88ZNcm3E   Meet the Cast:   Ash Blair-Borders as our Dungeonmaster @doktormod on Mastadon   Jeff Richardson as Sticks @eljefetacoma on twitter   Kristy Ellen as Matthias @kmitko3 on Mastadon Read her stories at: https://vocal.media/authors/kristy-ockunzzi-kmit   Sako as Chester Langrave @psychosako on socials Sako and the Moon Podcast: https://www.youtube.com/@SakoandtheMoonPodcast     Theme Song: I'm Taking Off (Shield Your Eyes) by Space Knife released July 4, 2019, used under license, all rights reserved.   Get involved! Contact us: By email: shatteredworldspod@gmail.com or Twitter: @swrpgpod   Get your name on the show! Use the hashtag #swearpig when you tweet about us and we'll name a character after you!   Like our Facebook page - Shattered Worlds RPG

With nicer weather continuing to develop, and the school year approaching its end, many people will be looking for day trips getaways in our area. WNYC and Gothamist transportation reporter Stephen Nessen shares his tips for public transit options to help you plan your trip, and listeners call in to share their favorite day trips.

Open Innovation has become essential to good science being completed by our institutions.  Our guest this week, Steve Rader speaks with experience on how open innovation, rather crowdsourcing has and will shape good science work moving forward. Steven recently retired from NASA and is an expert in open innovation after 36 years with NASA at the Johnson Space Center in Houston, TX.   We discussed his experiences leveraging crowdsourcing and open innovation to achieve significant cost savings and accelerate technological advancements at NASA,  including nearly 900 successful challenges within his career.  He highlighted the importance of understanding a problem's root causes before brainstorming solutions and the four "G's" driving participation (Gold, Guts, Glory, and Good) by thousands of our neighbors passionate about the work of science.   Our listeners can connect with Steve by clicking here. This Are We Here Yet? podcast is in association with the Innova802 podcast.

What if the things we see in the sky aren't arriving from elsewhere, but are being conjured—stitched together by stress, belief, and shared attention? Could neural implants, collective memory, and ritual intent really summon forms into physical space? And if so, who—or what—is steering the story?​​If you are having a mental health crisis and need immediate help, please go to ​https://troubledminds.org/help/ and call somebody right now. Reaching out for support is a sign of strength.​​LIVE ON Digital Radio! Http://bit.ly/40KBtlW​​http://www.troubledminds.net or ​https://www.troubledminds.org​​Support The Show!​https://www.spreaker.com/podcast/troubled-minds-radio--4953916/support​https://ko-fi.com/troubledminds​https://patreon.com/troubledminds​https://www.buymeacoffee.com/troubledminds​https://troubledfans.com​​Friends of Troubled Minds! - ​https://troubledminds.org/friends​​Show Schedule Sun--Tues--Thurs--Fri 7-10pst​iTunes - ​https://apple.co/2zZ4hx6​Spotify - ​https://spoti.fi/2UgyzqM​TuneIn - ​https://bit.ly/2FZOErS​Twitter - ​https://bit.ly/2CYB71U​----------------------------------------​​https://troubledminds.substack.com/p/crowdsourcing-the-unknown-specters​​https://www.earth.com/news/rethinking-vision-the-brain-sees-what-it-wants-to-see/​​https://www.nature.com/articles/s41467-025-58707-4​​https://rationalwiki.org/wiki/Thomas_E._Bearden​​https://rationalwiki.org/wiki/Scalar_wave​​https://en.wikipedia.org/wiki/Scalar_field_theory​​http://www.rexresearch.com/bearden/beardenbooks.html​​https://www.theguardian.com/science/2025/apr/18/scientists-claim-to-have-found-colour-no-one-has-seen-before​​https://www.bookaneyetest.co.uk/media/032phamy/close-objects-appear-further-away.png

with @ilblackdragon @rhhackettWelcome to web3 with a16z. I'm your host, Robert Hackett.In this episode, we're diving deep into one of the most intriguing intersections in tech today: AI and crypto.To help us unpack it, we're joined by Illia Polosukhin — co-founder of the crypto protocol NEAR and co-author of the groundbreaking 2017 "transformers" paper that kicked off the current AI boom. Ilia has been early to some of the biggest recent tech trends, and today he brings us a rare, panoramic view of the tech industry's cutting edge.Together we explore what the phrase “user-owned AI” really means; why the so-called agentic internet — that is, a world where your AI assistant talks directly to services on your behalf — might replace the very notion of websites and apps as we know them; and much more.Timestamps:(0:00) Introduction(3:40) Centralization and Challenges of AI(6:17) "User-Owned" AI(12:14) Confidential Computing and AI(17:51) The Birth of Transformers(22:33) NEAR AI and Crowdsourcing(27:56) AI Agents and Future Applications(31:04) The End of Websites and Applications(34:08) Dead Internet Theory & Distinguishing Humans(41:49) Open Source vs. Open Weight Models(43:48) Geopolitical Implications of AI(46:55) NEAR Protocol and Blockchain Scaling(59:29) The Role of Humans in an AI WorldResources:Attention is all you need by Vaswani et al. (Conference on Neural Information Processing Systems 2017)As a reminder, none of the content should be taken as investment, business, legal, or tax advice; please see a16z.com/disclosures for more important information, including a link to a list of our investments.

Philippe is the Founder of CrowdSec, an open-source multiplayer firewall that analyzes visitor behavior and provides an adapted response to all kinds of attacks. It leverages crowdsourced power to generate a global IP reputation database that protects the user network. As of today, CrowdSec boasts 250k+ user installations from 185+ countries and 50M+ malevolent IPs blocked. The CrowdSec community's users include governments, major e-commerce actors, media and financial institutions, armed forces, universities, hospitals, research centers, and others—the company raised $15M+ in series A funding just last year! The company's ingenious software is built on the idea of “safer together.” Not only does CrowdSec block individual user attacks, but it also identifies each malevolent IP address during an attack. It uses this information to protect everyone in the CrowdSec community from future attacks. Philippe received an MBA in Computer Sciences from EPITA. He has created five start-up companies and is a seed investor in ten others. He is on the front line of major innovations in tech use and security, and he loves to share his wealth of knowledge in podcasts and public speaking events. Philippe loves to discuss: The most significant issue facing cybersecurity is how open-source cybersecurity platforms combat them. Why multiplayer firewalls can help limit zero-day attacks and minimize cyberwar from attempting to “divide and conquer” businesses. Why does Philippe believe malevolent IP attacks are growing in size, and how can everyday users equip themselves to protect their data? https://crowdsec.net/ https://www.linkedin.com/in/philippehumeau/?originalSubdomain=fr&original_referer=https%3A%2F%2Fwww.google.com%2F

What if the best idea for your business isn't coming from your C-suite, but from the intern who barely speaks in meetings? In this episode of The Liquid Lunch Project, hosts Matthew R. Meehan and Luigi “The Professor” Rosabianca sit down with Nick Jain, CEO of IdeaScale, to talk about shaking up the way businesses innovate. Nick drops no-BS insights on how small businesses can harness the power of crowdsourcing ideas, why financial fluency is non-negotiable, and how poker skills can make you a better entrepreneur.    Oh, and he's got a free tool for you small business owners that'll change how you brainstorm—stick around for that.   Episode Highlights: IdeaScale 101: Learn how this “social network for ideas” lets businesses crowdsource game-changing ideas from employees, customers, and beyond. Real-World Win: A global restaurant chain used IdeaScale to crowdsource their next big menu item—and it wasn't just a PR stunt. Big Company vs. Small Company Life: Nick spills the tea on why running a smaller company is like driving a speedboat—faster, riskier, and way more fun. Idea Meritocracy: Why the best ideas should win, not the loudest voice or the highest-paid suit.  Financial Fluency: Nick explains why every entrepreneur needs to master financial statements—or risk driving blind. Poker and Business: How calculated risks at the poker table mirror smart business moves. Free Tool Alert: IdeaScale is free for businesses under 100 people—zero strings attached.  Who is Nick? Nick Jain is the CEO of IdeaScale, a social network for ideas that helps businesses crowdsource innovation. A Harvard Business School grad and former Bain Capital hotshot, Nick's now running a midsize software company while juggling fatherhood, a computer science degree, and fixing electrical outlets at his rental properties.  Take Action: Want to stop chasing your tail and start innovating like a badass? Tune in to hear Nick Jain drop knowledge bombs that'll make you rethink how you run your business. Plus, snag a free tool that'll have your team's best ideas bubbling up faster than a cold beer on a hot day. Listen now—you'll thank us later. Favorite Quote: “If you can't read financial statements, you have no idea how your business is doing. It's like driving with covers on your windshield. You'd never do it.” Connect with Nick: X: https://x.com/NickMJain LinkedIn: https://www.linkedin.com/in/nickjain/ Website: https://ideascale.com   Like what you heard? Don't forget to subscribe, rate, and review!

How do you transform a successful corporate career into a thriving business that builds communities and empowers individuals? In this episode of Life After Corporate, host Debra Boulanger kickoffs off the Expert Community Builders series by interviewing  Cate Luzio, Founder and CEO of Luminary, a global membership platform and professional ecosystem. Cate shares how she transitioned from corporate finance into community building, scaled a membership business without investors, and built an inclusive ecosystem that bridges entrepreneurs, business leaders, and corporate sponsors. The conversation spans Cate's initial inspirations, the challenges of pivoting in a pandemic, the strategic growth through strategic acquisitions, and the crucial role of inclusivity in building impactful communities. Cate's insights reveal how vulnerability in business is a strength and how agile adaptability fuels success.  This episode promises inspiring and practical insights for those eager to grow into successful leaders in the ever-changing digital landscape.    [3:00 – 05:30]  The Meaning of Community & Luminary's Mission Shared access and intentional networking Redefining community beyond daily engagement Investing in relationships as a success strategy Creating ecosystems of support for professional women     [05:45 – 08:40] Early Struggles in Scaling a New Membership Platform Building brand awareness organically, without paid ads Differentiating Luminary from coworking spaces or women's clubs Learning not to take business personally Crowdsourcing member-driven programming topics       [08:40 – 13:40]   Strategic Growth Through Acquisitions and Ecosystem Expansion Acquired Declare, The Crew, and HeyMama to deepen value Built a national partner network to scale physical presence Stayed self-funded to maintain strategic freedom Chose acquisitions that aligned with Luminary's mission    [20:10 – 25:00] Leading with Core Values & Navigating Category Challenges Scaling while staying true to mission and values Differentiating Luminary amidst industry disruption Thought leadership on building sustainable community categories Balancing personal leadership with team-wide vision    Contact Cate: Instagram: @cateluzio LinkedIn: https://www.linkedin.com/in/cluzio   Luminary:  https://www.weareluminary.com/home Luminary is going on the road!!! Click here to find out where they are going https://www.weareluminary.com/luminarylive2025    Ready to turn insights into action? Don't just listen—join the movement! The Life After Corporate Community (https://lifeaftercorporate.com/community) is where ambitious women like you connect, collaborate, and get the strategies, tools, and high-level support to grow a thriving, profitable business. Join us now and start making the powerful connections that will elevate your success! https://lifeaftercorporate.com/community   Other episodes you may enjoy; find them at: https://lifeaftercorporate.com/podcast/  or https://www.pod.link/1500631278 205. Common Trademark Mistakes That Could Cost Your Business Thousands 197.  How Meghann Conter is Rewriting the Rules of Success for Women Entrepreneurs with THE DAMES 194.  How Entrepreneurs Can Avoid Becoming Seven-Figure Poor 19. Pivoting During a Pandemic and Leading With Your North Star, with Cate Luzio - Founder of Luminary   Tweetable Quotes: "But the nice thing about it was I wasn't alone. You know? Everybody was going through some version of how it was impacting them...".... Cate Luzio on the role of community "You will always, if you build community right, get something in return. But you've gotta give, and you and then you can also take."...Cate Luzio on community engagement   **TRANSCRIPT AVAILABLE UPON REQUEST**   SUBSCRIBE & LEAVE A FIVE-STAR REVIEW and share this podcast to other growing entrepreneurs!  Get weekly tips on how to create more money and meaning doing work you love and be one of the many growing entrepreneurs in our community. Connect with me on LinkedIn; https://www.linkedin.com/groups/12656341/  or on Instagram or our website at www.lifeaftercorporatepodcast.com .

Sam and Sierra answer a letter from someone who posted a picture of the guy she was dating in a "Is This Your Man" forum and it kind of blew up on her. Join us on Patreon for an extra weekly episode, monthly office hours, and more! SUBMIT: justbreakuppod.com FACEBOOK: /justbreakuppod INSTAGRAM: @justbreakuppod Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of 'A Duty to Act', Jennifer Darling and her husband Josh discuss the importance of activating communities through social media for public service agencies. They explore the need for effective marketing strategies, the significance of establishing a unique brand identity, and the role of authenticity in social media personas. The conversation also covers various content creation strategies to engage the community and highlights the types of content that can be effective for public service agencies while cautioning against content that could harm the department's reputation. The conversation delves into the importance of understanding and engaging with the community through social media, emphasizing the need for targeted content creation, effective use of various platforms, and the establishment of clear social media policies. The speakers discuss strategies for crowdsourcing content, maximizing the longevity of posts, and the significance of regular engagement to build relationships with the community.Social media is essential for community engagement.Public service agencies must market themselves effectively.Brand identity goes beyond logos; it's about mission and values.Establishing a voice on social media is crucial.Authenticity in social media personas builds trust.Video content is more engaging than static images.Community engagement can enhance recruitment efforts.Content should reflect the department's ethos.Avoid posting content that could damage the department's reputation.Engagement strategies should focus on showing the human side of public service. Understanding your audience is crucial for effective communication.Creating personas based on community demographics can enhance content relevance.Crowdsourcing content can significantly boost engagement and variety.Engaging with the community through social media fosters trust and connection.Establishing a clear social media policy is essential for managing content and interactions.Less than 30% of nonprofits have a social media policy, highlighting a gap in the sector.YouTube offers the longest content lifespan compared to other platforms.Regular posting helps maintain visibility and community engagement.Not every post needs to be high production; authenticity matters.Building relationships with the community enhances support during critical times.

Keywords:  entrepreneurship, scaling, trucking industry, shoe business, private equity, business strategy, e-commerce, rental marketplace, leadership, professional growth, failure, learning, Ideascale, innovation, government, software, small business, project management, AI, entrepreneurship Summary:  In this episode, Mitch Beinhacker interviews Nick Jain, a professional CEO with a diverse background in various industries including trucking and e-commerce. They discuss Nick's journey from Wall Street to becoming a CEO, the challenges of scaling organizations, and the intricacies of the trucking industry. Nick shares insights on the importance of governance and systems in scaling businesses, as well as his experience in the shoe rental market, which ultimately faced challenges due to market dynamics and consumer behavior. In this conversation, Nick Jain discusses the lessons learned from business failures, the inception and growth of Ideascale, and the importance of innovation in both private and public sectors. He emphasizes the need for organizations to foster creativity, provide incentives for innovation, and utilize effective software solutions to manage ideas. Jain also highlights Ideascale's commitment to supporting small businesses by offering free access to their platform, which helps organizations collect, evaluate, and implement ideas effectively. The discussion concludes with insights into upcoming features and the role of AI in enhancing the platform's capabilities. Takeaways Scaling requires good governance and HR policies. Not all startups are ready to scale effectively. A professional CEO can bring necessary skills to a company. Trucking is a complex industry with many operational challenges. The importance of a systematic pricing strategy in trucking. E-commerce can be a viable entry point for technology-driven businesses. Subscription models can be valuable but require careful analysis. Understanding consumer behavior is crucial for business success. Emotional decision-making can impact financial choices. Analytics play a key role in inventory management for retail. Failure teaches us invaluable lessons. Understanding unit economics is crucial for success. Ideascale helps organizations innovate effectively. Crowdsourcing ideas can lead to better solutions. Security and control are essential for large organizations. Incentives are necessary to encourage innovation. Small teams can access Ideascale for free. Implementation of ideas requires structured processes. APIs enable seamless integration of software solutions. Using existing software is often more efficient than building new. Titles Navigating the Entrepreneurial Landscape with Nick Jain From Wall Street to Trucking: A CEO's Journey Scaling Businesses: The Key to Sustainable Growth The Challenges of the Trucking Industry Explained Innovating in E-commerce: Lessons from the Shoe Business Sound Bites "I'm a professional CEO." "You need backups in your organization." "The business did fail ultimately." "We learn 10 times more from failure." "Get analytical on the unit economics." "Crowdsourcing ideas is key." "Security and control are crucial." "You need incentives to innovate." "Our software is free for small teams." "We eat our own cooking." "APIs have changed the world." Chapters 00:00 Introduction and New Beginnings 04:08 Nick Jain's Professional Journey 05:44 The Importance of Scaling Organizations 08:17 Transitioning from Wall Street to Trucking 10:11 Challenges in the Trucking Industry 18:38 Exploring the Shoe Business 24:05 Lessons from the Shoe Rental Business 28:14 Learning from Failure 32:23 The Birth of Ideascale 34:46 Innovation in Government Organizations 40:51 Why Choose Ideascale? 43:54 Keys to Organizational Innovation 47:01 Free Access for Small Businesses 49:56 Implementation of Ideas 52:44 What's Next for Ideascale?

The federal government has just announced a major expansion to their Medicare urgent care clinics across the country so we asked the Health Minister what it'll mean for families across Australia. Plus, are you one to crowdsource answers to some of the biggest (and smallest) decisions in your path? You might've tuned out your intuition. We explore why many of us have lost touch with our gut feelings and the surprisingly simple ways we might reconnect with this underrated internal compass. THE END BITS Support independent women's media Check out The Quicky Instagram here Buy tickets to The Mamamia Out Loud LIVE ALL OR NOTHING TOUR HERE: http://outloudlive.com.au/ GET IN TOUCH Share your story, feedback, or dilemma! Send us a voice note or email us at thequicky@mamamia.com.au CREDITS Hosts: Taylah Strano Guests: The Hon. Mark Butler, Minister for Health & Aged Car Ailish Delaney, Mamamia News Writer Executive Producer: Taylah Strano Audio Producers: Lu Hill Become a Mamamia subscriber: https://www.mamamia.com.au/subscribeSee omnystudio.com/listener for privacy information.

Best Ball Breakfast returns as I draft two more teams in the 2025 Big Board contest on Underdog with a $250k top prize. I also upset the "Coke heads" while landing on my first big exposure stand of the year. ⁠⁠Watch stream here⁠.⁠☕ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Become a "Best Ball Value Hound" Youtube member⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to get access to Best Ball After Dark interviews and unlock the #☕bestball-breakfast channel in the Deposit Kingdom Discord where I'll tip when I'm joining drafts.

Locate, access, and trust the data you need using Microsoft Purview's Unified Catalog. By leveraging AI-powered search and automated quality checks, you can use data across your organization while staying compliant and meeting privacy standards. With streamlined approval workflows, request and gain access to data quickly, collaborate with stakeholders, and ensure data quality across projects.  Daniel Hidalgo, Microsoft Purview Senior Product Manager, joins Jeremy Chapman to share how to manage data governance, drive better decisions, and support meaningful AI outcomes with Microsoft Purview.   ► QUICK LINKS: 00:00 - Microsoft Purview Data Governance 00:29 - Data visibility and access 01:46 - Universal data catalog 03:23 - Crowdsourcing approach 04:24 - Business user demo 06:58 - How it works 08:05 - Create new governance domain 08:53 - Define data products 09:39 - Automate data quality checks 10:33 - Day-to-day management 11:57 - Wrap up   ► Link References Get started at https://aka.ms/PurviewDataGovernance     ► Unfamiliar with Microsoft Mechanics?  As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast   ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics  • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

For better or worse, I drafted my first teams in the 2025 Big Board contest on Underdog with a $250k top prize. Some people hated them, some people loved them. Just kidding, no one loved them. ⁠Watch stream here.⁠☕ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Become a "Best Ball Value Hound" Youtube member⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to get access to Best Ball After Dark interviews and unlock the #☕bestball-breakfast channel in the Deposit Kingdom Discord where I'll tip when I'm joining drafts.

In this conversation, Tom the Producer reports to Beacon founder, Meg McCormick Hoerner on adding various fundraising initiatives to the website of the Bridgeton Beacon.They discuss local media, working with Bridgeton youth, and a new project called "Memory Podcasts", which allows you to sponsor the recording session for a local beacon of the community.The dialogue explores the legacy of Paul Hunsberger and the importance of preserving local history through storytelling. The speakers highlight the potential of using AI to enhance storytelling and the significance of crowdsourcing community stories to connect the past with the present.takeawaysThe Bridgeton Beacon now has multiple fundraising channels to support its initiatives.Engaging the community through memory projects can foster local connections.Preserving local history is essential for future generations.Every individual's story is valuable and worthy of preservation.Utilizing technology can enhance the storytelling process.Crowdsourcing stories can help build a richer community narrative.The legacy of influential figures like Paul Hunsberger is crucial to local history.AI can assist in organizing and presenting community stories effectively.Community contributions should directly support local content production.Encouraging feedback from the community can guide future projects.Chapters00:00 Introduction to Bridgeton Beacon's Initiatives02:18 Fundraising Channels and Community Engagement04:25 Memory Podcasts: Preserving Local History07:20 The Importance of Oral History10:00 Community Contributions and Nonprofit Goals12:38 Facilitating Conversations Across Generations15:24 Leveraging AI for Engaging Interviews19:19 Exploring Creative Questions for Engagement21:50 Innovative Ideas for Memory Preservation24:18 The Hunsberger Project: A Legacy of Stories29:48 Building a Digital Archive of Memories35:23 Connecting Past, Present, and Future Stories

In this conversation, producer Tom Ritter reports to Beacon founder, Meg McCormick Hoerner on adding various fundraising initiatives to the website of the Bridgeton Beacon. They discuss local media, working with Bridgeton youth, and a new project called "Memory Podcasts", which allows you to sponsor the recording session for a local beacon of the community. The dialogue explores the legacy of Paul Hunsberger and the importance of preserving local history through storytelling. The speakers highlight the potential of using AI to enhance storytelling and the significance of crowdsourcing community stories to connect the past with the present. takeaways The Bridgeton Beacon now has multiple fundraising channels to support its initiatives. Engaging the community through memory projects can foster local connections. Preserving local history is essential for future generations. Every individual's story is valuable and worthy of preservation. Utilizing technology can enhance the storytelling process. Crowdsourcing stories can help build a richer community narrative. The legacy of influential figures like Paul Hunsberger is crucial to local history. AI can assist in organizing and presenting community stories effectively. Community contributions should directly support local content production. Encouraging feedback from the community can guide future projects. Chapters 00:00 Introduction to Bridgeton Beacon's Initiatives 02:18 Fundraising Channels and Community Engagement 04:25 Memory Podcasts: Preserving Local History 07:20 The Importance of Oral History 10:00 Community Contributions and Nonprofit Goals 12:38 Facilitating Conversations Across Generations 15:24 Leveraging AI for Engaging Interviews 19:19 Exploring Creative Questions for Engagement 21:50 Innovative Ideas for Memory Preservation 24:18 The Hunsberger Project: A Legacy of Stories 29:48 Building a Digital Archive of Memories 35:23 Connecting Past, Present, and Future Stories

One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro

The Military Intelligence required to land on the beaches of Normandy France in 1944 was massive. And while Allied Forces were up to the task in gathering terrain maps, soil samples and German defensive positions, a TON of valuable information was gained through a BBC Photography contest and photos submitted by the public. In this episode, we talk about different types of intelligence gathering, how they're used in modern warfare, and this ingenious idea for gaining information that helped plan the landings on D-Day. Then we chat with Comedian and podcast regular, Jay Black! Review this podcast at https://podcasts.apple.com/us/podcast/the-internet-says-it-s-true/id1530853589 Bonus episodes and content available at http://Patreon.com/MichaelKent For special discounts and links to our sponsors, visit http://theinternetsaysitstrue.com/deals

How do you keep your team energized, engaged, and aligned—especially in the demanding world of social impact? Passion and purpose are key, but they're not enough on their own. In this episode, Eric and Jonathan unpack the essential elements of team motivation, exploring why some teams thrive while others stall, and revealing the missing ingredient in traditional approaches to workplace fulfillment. Plus, hear how strategies like impact storytelling, feedback systems, and even gym breaks can transform your culture and keep burnout at bay. Ready to inspire your team? Let's get into it.Episode Highlights:[00:00] Opening ThoughtsThe unique challenges and opportunities for social impact leaders.[03:41] Breaking Down Team MotivationThe differences between highly motivated teams and struggling ones.[06:16] Adding Balance to the EquationHow burnout is more than just a workload issue—it's a balance issue.[08:06] Meaning and Impact: Telling Your StoryJonathan's new strategy: tasking a team member with collecting 30 impact stories a year.[12:05] Individual Roles MatterHow to build motivation by recognizing the unique value of every team member.[17:48] Compensation: A Foundational FactorA quick reminder: pay matters, and it's a crucial piece of the puzzle.[18:06] Passion Plans: A New Idea for LeadersJonathan's concept of “passion plans” to help employees deepen their connection to the work.[24:33] Can You Nurture Other Parts of Your Life?How balance and personal growth amplify professional motivation.[30:32] Leadership's Role in MotivationMotivation is personal: How leaders can uncover individual drivers.[34:03] Closing ReflectionsResources:Ikigai DiagramUnexpected Ways a Rebrand Can Supercharge Your Team CultureYour Theory of Change isn't finished until your Grandma can understand it.8 Ways To Make Your Nonprofit Storytelling Stand OutHow to use Crowdsourcing to Fuel Your Nonprofit's Digital Content StrategyWhy Most Activation Plans Fail and How to Fix Yours.The Benefits of a 4 Day Work WeekListeners, now you can text us your comments or questions by clicking this link.*** If you liked this episode, please help spread the word. Share with your friends or co-workers, post it to social media, “follow” or “subscribe” in your podcast app, or write a review on Apple Podcasts. We could not do this without you! We love hearing feedback from our community, so please email us with your questions or comments — including topics you'd like us to cover in future episodes — at podcast@designbycosmic.com Thank you for all that you do for your cause and for being part of the movement to move humanity and the planet forward.

ITP - 110 focuses on the unique challenges and opportunities for teachers over 50 seeking international teaching positions. Hosted by Greg, JP Mint, and Kent, with guests Stacey Stephens and Lissa Layman from TIE Online, the discussion introduces TIE's new Over 50 Guide. This resource aims to support older educators by addressing age-related hiring limitations, navigating country-specific regulations, and highlighting the importance of lifelong learning and adaptability. The guide is a collaborative, evolving effort based on crowdsourced data from educators and administrators, offering practical tips such as demonstrating ongoing professional development and planning career trajectories with age restrictions in mind. A key takeaway is the need for over-50 educators to recalibrate their job expectations, expand their search to less conventional regions, and leverage networking for new opportunities. The podcast highlights that 62% of international schools registered with TIE report hiring teachers over 50 or 60, dispelling the notion that international teaching is limited to younger educators. Future plans include expanding guides to address other demographics and regions, with a focus on underserved groups. The episode underscores TIE Online's commitment to fostering an inclusive international education community by providing real-time data, professional growth tools, and actionable resources for educators at all stages of their careers. Sound Bites "Plan backwards for your career." "Recalibrate your expectations for salary." "Seek high quality health care as you age." "Crowdsourcing information is key for teachers." "Lean into your experience and value." "My peer group is retiring." "It's difficult to maintain a social life." "Where are the countries that hire over 60?" "62% of schools have no age limits." "How do we serve the underserved?" (00:00) Introduction to the International Teacher Podcast (02:23) The Over 50 Teacher's Guide (04:07) Challenges Faced by Teachers Over 50 (09:13) Crowdsourcing Information for Teachers (11:59) Top Tips for Teachers Over 50 (18:39) Navigating Ageism in International Teaching (26:49) The Importance of Networking for Older Educators (33:05) Navigating Retirement and Social Connections (35:51) Resources for Over 50 International Teachers (37:17) The Safety Net of Domestic Teaching vs. International Teaching (39:45) Data-Driven Decisions for International Teaching (40:23) Updating Recruitment Strategies for Experienced Educators (42:10) Regions of Opportunity for Over 50 Educators (44:28) Quality of Life Considerations for International Educators (46:03) The Importance of Financial Independence (49:14) Membership Benefits and Resources for Educators (52:06) Future Guides and Resources for Diverse Demographics (55:45) Final Thoughts and Community Engagement The International Teacher Podcast is a bi-weekly discussion with experts in international education. New Teachers, burned out local teachers, local School Leaders, International school Leadership, current Overseas tTeachers, and everyone interested in international schools can benefit from hearing stories and advice about living and teaching overseas. Additional Gems Related to Our Show: Greg's Favorite Video From Living Overseas - https://www.youtube.com/watch?v=UQWKBwzF-hw Signup to be our guest  https://calendly.com/itpexpat/itp-interview?month=2025-01 Our Website⁠ -  https://www.itpexpat.com/ Our FaceBook Group - https://www.facebook.com/groups/itpexpat ⁠JPMint Consulting Website  - https://www.jpmintconsulting.com/ Greg's Personal YouTube Channel: https://www.youtube.com/playlist?list=PLs1B3Wc0wm6DR_99OS5SyzvuzENc-bBdO Books By Gregory Lemoine: ⁠International Teaching: The Best-kept Secret in Education by Gregory Lemoine M.Ed. ⁠Finding the Right Fit: Your Professional Guide for International Educator Recruiting Fairs and Amazing Stories of a Teacher Living Overseas⁠ by Gregory Lemoine M.Ed.

CrowdHealth CEO Andy Schoonover shares his journey of disrupting the healthcare industry with a revolutionary crowdsourcing model that puts patients first. In This Episode: 00:06 - The Birth of Crowd Health 01:30 - Personal Healthcare Struggles 02:17 - The Cost of Medical Procedures 03:27 - Challenges with Health Insurance 03:40 - Starting Crowd Health 03:56 - Early Customers and Growth 04:26 - Healthcare Industry Insights 05:58 - Crowdsourcing in Healthcare 08:59 - The War with Big Insurance 11:19 - Elevator Pitch for Crowd Health 13:21 - Market Forces in Healthcare 17:21 - Cash-Based Healthcare Benefits 24:34 - Customer Acquisition Strategies 31:41 - The Big Health Event 31:46 - Telling Stories: Real-Life Health Journeys 32:12 - Cost-Saving Healthcare Solutions 35:14 - Incentivizing Metabolic Health 43:15 - Community-Based Healthcare 53:23 - Entrepreneurial Insights and Future Goals The Health Hustle Newsletter - Marketing Tips for Health Brands⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Niche Test⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Get all links, resources, and show notes at: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.coreyhi.com/podcast/181

Draft tandem Jeremy Nygaard and JD Cameron team up for a podcast to discuss prospects on their way to the big leagues and the MLB draft, produced by Theo Tollefson. 0:00 Intro 1:45 Blake Snell to the Dodgers 4:15 Boyd to the Cubs; Montas to the Mets 17:30 Draft Lottery Preview 19:23 What is the Purpose of the Lottery? 24:40 Do Teams Actually Move Up? 26:26 How Does it all work? 27:56 What are the Wrinkles? 34:40 What are the chances the Brewers, Cubs and Twins move up? 44:14 Reveal of Comp Picks at the Lottery too. 46:56 Crowdsourcing 55:43 Outro You can support the show by downloading it from wherever you get your podcasts, including iTunes and Spotify. If you enjoy the content, consider leaving us a five-star rating and review in addition to sharing or retweeting DTS-related content. You can follow us on Twitter @DTS_POD1, @Jeremynygaard, @J_D_Cameron, and @TheodoreTollef1. You can also find full episodes and clips of our shows on our YouTube page @DestinationTheShow.  

Draft tandem Jeremy Nygaard and JD Cameron team up for a podcast to discuss prospects on their way to the big leagues and the MLB draft, produced by Theo Tollefson. 0:00 Intro 2:43 Baseball! Jax as a starter? 11:45 The Angels are doing stuff? 16:30 Crowdsourcing question from last week 22:45 Brewers Needs 28:22 Cubs Needs 36:17 Twins Needs 43:11 Listener Questions 47:41 Housekeeping You can support the show by downloading it from wherever you get your podcasts, including iTunes and Spotify. If you enjoy the content, consider leaving us a five-star rating and review in addition to sharing or retweeting DTS-related content. You can follow us on Twitter @DTS_POD1, @Jeremynygaard, @J_D_Cameron, and @TheodoreTollef1. You can also find full episodes and clips of our shows on our YouTube page @DestinationTheShow.

Today I'm thrilled to be joined by a true industry pioneer who's reshaped the world of freelancing and online financing - Matt Barrie. Matt is the founder of Freelancer.com, the world's largest freelancing and crowdsourcing marketplace. Freelancer has empowered and connected 77 million of professionals and businesses worldwide. Freelancer is a listed company that has reached a market cap of over $1 billion. In addition to that, his other business Escrow.com has revolutionised secure online transactions, Escrow.com is the world's largest escrow company. It's like paypal but for big ticket items. $7 billion of volume. Buying boats, business, airplanes. Like when facebook changed their domain to meta.com they bought the domain and trademarks through Escrow.com, instagram.com to instagram, uber.com to uber, you name it.And lastly, his third business Loadshift, which is Australia's largest heavy haulage transport marketplace, which moves as much freight in distance from the earth to the moon, everyday. Matt's an old friend of mine that helped me in the early days and has always been a big supporter of entrepreneurship so I'm super excited to catch up.Actually btw - Matt doesn't do anything in halves and likes to go against the grain, which is why he's so brilliant, so we're going to go rogue and change up the format Hosted on Acast. See acast.com/privacy for more information.

Guest Thomas Karagianes | Jonathan Romano Panelist Richard Littauer Show Notes In this episode, host Richard Littauer discusses the journey and impact of Eterna with developers Jonathan Romano and Thomas Karagianes. The conversation revolves around Eterna's role in RNA research through user-contributed puzzle solutions, emphasizing community engagement and educational outreach. Topics include the integration of hybrid intelligence, where human intuition complements AI in scientific discovery, and the significance of explainable AI in motivating player participation. The episode also touches on the ethical considerations in collaborating with for-profit entities, the development of accessible COVID vaccines, and low-cost tuberculosis diagnostics. Hit download now to hear more! [00:01:24] Jonathan describes Eterna, a platform where players solve puzzles to contribute to RNA research. [00:02:12] Thomas explains that Eterna focuses on RNA complexity and its importance in modern science, like mRNA vaccines and how Eterna engages players in folding RNA sequences and testing them in labs. [00:04:36] Richard asks if the project is open source and Jonathan says its partially open source and explains the technical limitations that prevent full openness. [00:05:26] We learn about Eterna's community with around 100,000 total players, and a core group of about 30-40 who regularly engage in scientific challenges. [00:07:31] Thomas discusses ongoing efforts to make the game more accessible and increase community engagement through educational outreach and simplifying the tutorial system, and Eterna is used in classrooms as a teaching tool. [00:09:47] Jonathan explains how some Eterna players become code contributors, staff members, and even lead authors on academic papers. [00:13:32] We hear about the funding of the community. [00:15:56] Thomas discusses how Eterna integrates AI to assist players but stresses the importance of human intuition in tackling unique challenges and Jonathan explains how Eterna uses hybrid intelligence, combining AI and human input for better research outcomes. He highlights how Eterna's community has contributed to important research, including COVID-19 vaccine development and tuberculosis diagnostics. [00:22:29] Thomas shares that Eterna attracts players who enjoy breaking the model or exploring boundaries, making the game engaging and motivating for them. [00:27:48] Jonathan and Thomas discuss the ethical considerations of partnerships, especially with for-profit companies, and the need to engage the community in decision-making processes. [00:31:41] Jonathan shares how you can contribute to Eterna and how to join the developer community on GitHub. Quotes [00:10:10] “Minimally, whenever there is a scientific publication that comes out of Eterna from players contributions, there is a consortium author on the paper. That will include everyone who has submitted a solution.” [00:14:21] “There's definitely this pattern - and you can even see it in the code- where open source code passes from grad student to grad student.” [00:19:14] “Hybrid intelligence is an underused buzzword.” Spotlight [00:33:16] Richard's spotlight is The Internet Archive. [00:34:23] Jonathan's spotlight is txircd, a modular IRC daemon written in Python. [00:35:32] Thomas's spotlight is Bioconda. Links SustainOSS (https://sustainoss.org/) podcast@sustainoss.org (mailto:podcast@sustainoss.org) richard@sustainoss.org (mailto:richard@sustainoss.org) SustainOSS Discourse (https://discourse.sustainoss.org/) SustainOSS Mastodon (https://mastodon.social/tags/sustainoss) SustainOSS LinkedIn (https://www.linkedin.com/company/sustainoss/?trk=public_profile_volunteering-position_profile-section-card_full-click&originalSubdomain=in) Open Collective-SustainOSS (Contribute) (https://opencollective.com/sustainoss) Richard Littauer Socials (https://www.burntfen.com/2023-05-30/socials) Thomas Karagianes LinkedIn (https://www.linkedin.com/in/thomaskaragianes/) Jonathan Romano Website (https://luxaritas.com/) Jonathan Romano LinkedIn (https://www.linkedin.com/in/luxaritas/) Eterna (https://eternagame.org/) Eterna Project Information (https://eternagame.org/about) Eterna OpenVaccine (https://eternagame.org/challenges/10845741) Eterna OpenTB (https://eternagame.org/challenges/10845742) Eterna OpenKnot (https://eternagame.org/challenges/11843006) Eternagame-GitHub (https://github.com/eternagame) Foldit (https://fold.it/) RNA (https://en.wikipedia.org/wiki/RNA) Hybrid Intelligence (Springer Link article) (https://link.springer.com/article/10.1007/s12599-019-00595-2) Mapping Citizen Science through the Lens of Human-Centered AI (Human Computation article) (https://hcjournal.org/index.php/jhc/article/view/133) Practical recommendations from a multi-perspective needs and challenges assessment of citizen science games (PLOS ONE article) (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285367) Mountains Beyond Mountains by Tracy Kidder (https://en.wikipedia.org/wiki/Mountains_Beyond_Mountains) Internet Archive (https://archive.org/) txircd (https://github.com/elementalalchemist/txircd) Bioconda (https://bioconda.github.io/) Reamde by Neal Stephenson (https://en.wikipedia.org/wiki/Reamde) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guests: Jonathan Romano and Thomas Karagianes.

(00:00-7:25) Crowdsourcing the night game in CFB this Saturday. Tim and Jackson's wager. People mad as hornets over Georgia being on the outside looking in. What's the formula for these rankings?(7:26-14:42) Gambling plays for Thursday Night Football. Spread covering in CFB as we come down the stretch. Shane Beamer may be looking to run one up on Drink and the Tigers on Saturday.(14:43-20:37) Where is the hot spot for sports in the country when you include college and professional sports? Indianapolis is Jackson's dream sports city.(20:38-27:56) E-Mail of the Day Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(00:00-7:25) Crowdsourcing the night game in CFB this Saturday. Tim and Jackson's wager. People mad as hornets over Georgia being on the outside looking in. What's the formula for these rankings? (7:26-14:42) Gambling plays for Thursday Night Football. Spread covering in CFB as we come down the stretch. Shane Beamer may be looking to run one up on Drink and the Tigers on Saturday. (14:43-20:37) Where is the hot spot for sports in the country when you include college and professional sports? Indianapolis is Jackson's dream sports city. (20:38-27:56) E-Mail of the Day Learn more about your ad choices. Visit podcastchoices.com/adchoices

Elon Musk's Grok and Crowdsourcing Medical Data – Elon Musk invites the public to submit medical images to his AI, Grok, raising serious privacy concerns and questions about AI's role in medical diagnostics.Australia's Workforce Report on Healthcare Professionals – Louise and George discuss whether the Australian government's workforce report is reductive in its vision for the role of tech.Health Technology in Conflict Zones – Health technology innovations are transforming care in war zones, with tools like telemedicine and AI bridging gaps in access and resources for conflict-affected populations.The First Wearable to Kill Cancer Cells – A new FDA-approved wearable device uses electric fields to target and kill cancer cells without harming healthy cells, marking a significant step forward in digital cancer treatments.The results of the US election are in. What might be the impact on global health and digital innovation in health?Remote monitoring in the islands off Ireland gives Kate a trip down memory lane.World-Class Health Information Exchange – We welcome Dr. Robin Mann and Dr. Honor Magon, co-authors of a popular Pulse+IT opinion piece, to discuss the vision for a world-class Health Information Exchange and its potential impact.Visit Pulse+IT.news to learn more, engage in this rapidly growing sector, and subscribe to breaking digital news, weekly newsletters and a rich treasure trove of archival material. People in the know, get their news from Pulse+IT – Your leading voice in digital health news.Follow us on LinkedIn Louise | George | Pulse+ITFollow us on X Louise | George | Pulse+ITSend us your questions pulsepod@pulseit.newsProduction by Octopod Productions | Ivan Juric

Welcome to Small Business School! In this special episode I am bringing three incredible Canadian female business owners together who are transforming the world of ethical, sustainable, and size-inclusive fashion. We dive deep into their journeys of entrepreneurship, the challenges of building a brand, and how they manage to stay true to their values in a fast-changing industry!Topics Covered:The evolution of each guest's fashion brand and how they balance sustainability with growth.Managing customer expectations while staying true to the core values of ethical production.Crowdsourcing for product development and the power of supportive community.The importance of profitability, even in purpose-driven businesses.Managing relationships in manufacturing and the complexities that come with it.Shifting from a scarcity mindset to embracing collaboration.How collaborating with other businesses in promotions and cross-promoting newsletters led to unexpected success.The difficulties of customer acquisition without relying heavily on paid ads.This episode is full of actionable insights and heartwarming stories about the challenges and rewards of creating and scaling a sustainable and mission driven business. You don't want to miss it!Guest Info/Links:Candice Munro from Buttercream ClothingInstagramWebsiteJess Sternberg from Free LabelPersonal LinkedInInstagramWebsiteKristi Soomer from EncircledPersonal InstagramPersonal LinkedInInstagramWebsiteStaci's Links:Instagram. Website.The School for Small Business Podcast is a proud member of the Female Alliance Media. To learn more about Female Alliance Media and how they are elevating female voices or how they can support your show, visit femalealliancemedia.ca.Head over to my website https://www.stacimillard.com/ to grab your FREE copy of my Profit Playbook and receive 30 innovative ways you can add more profit to your business AND the first step towards implementing these ideas in your business!

Shoot us a Text.Happy Saturday! Nathan and Chris host the show today to recap the ASOTU team's time in Austin, TX this week and then share some of the good we're seeing in the auto industry.Park Place Dealerships donates $100,000 in small grant giveaways to local charities. The charities are submitted by employees and community members and in a couple of weeks the Irvine, TX community will learn who has won the grant awards.Hosts: Paul J Daly and Kyle MountsierGet the Daily Push Back email at https://www.asotu.com/ JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/ Read our most recent email at: https://www.asotu.com/media/push-back-email

Featuring Kris Rudeegraap, CEO of Sendoso, this conversation dives into the evolution of sales development, focusing on how Sendoso has leveraged AI and automation to enhance outbound sales efficiency and effectiveness. Kris shares valuable insights on tracking SDR activities, automating repetitive tasks, and utilizing intent data to prioritize high-value accounts. The discussion also covers the future of go-to-market strategies and the importance of organizations adapting to new technologies and methodologies. Takeaways 1. Kris Rudeegraap is the co-founder and CEO of Sendoso, a direct mail and gifting platform. 2. The evolution of outbound sales has necessitated new strategies and tools. 3. AI and automation can significantly enhance sales development efficiency. 4. Tracking SDR activities can reveal opportunities for automation. 5. Intent data is crucial for prioritizing high-value accounts. 6. Gifting can be an effective strategy for engaging prospects. 7. Organizations need to rethink traditional sales playbooks in light of new technologies. 8. Crowdsourcing ideas from team members can lead to innovative solutions. 9. Investing in AI training for employees can improve overall productivity. 10. The future of sales development will increasingly rely on AI and automation. ---- Join 2,500+ readers getting weekly practical guidance to scale themselves and their companies using uncommon advantages and Artificial intelligence. Sign up for Superhuman Revenue Newsletter here: https://superhumanrevenue.beehiiv.com/subscribe

We continue our discussion around theorizing about digital phenomena and publishing conceptual papers. Today, we are joined by , who has published several theoretical articles on digital technology in Academy of Management Review. He is also an AMR editor for a special issue on and he heads the Theory section as senior editor in the Journal of the Association for Information Systems. With Robert, we talk about the AMR publishing process, how it is different from mainstream IS journals and what we need to look out for when we generate theory about new digital phenomena. References Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021). The Role of Artificial Intelligence and Data Network Effects for Creating User Value. Academy of Management Review, 46(3), 534-551. Sieber, S., & Gregory, R. W. (2018). Facebook's Data Debacle in 2018. How to Move on? IESE Teaching Case, Number SI-200-E. Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021). Data Network Effects: Key Conditions, Shared Data, and the Data Value Duality. Academy of Management Review, 47(1), 189-192. Gregory, R. W., & Henfridsson, O. (2021). Bridging Art and Science: Phenomenon-Driven Theorizing. Journal of the Association for Information Systems, 22(6), 1509-1523. Afuah, A., & Tucci, C. L. (2012). Crowdsourcing as a Solution to Distant Search. Academy of Management Review, 37(3), 355-375. Fisher, G., Mayer, K. J., & Morris, S. (2021). From the Editors—Phenomenon-Based Theorizing. Academy of Management Review, 46(4), 631-639. Raisch, S., & Fomina, K. (2024). Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations. Academy of Management Review, . Baiyere, A., Berente, N., & Avital, M. (2023). On Digital Theorizing, Clickbait Research, and the Cumulative Tradition. Journal of Information Technology, 38(1), 67-73. Grover, V., & Lyytinen, K. (2023). The Pursuit of Innovative Theory in the Digital Age. Journal of Information Technology, 38(1), 45-59. Gregory, R. W., Beck, R., Henfridsson, O., & Yaraghi, N. (2024). Cooperation Among Strangers: Algorithmic Enforcement of Reciprocal Exchange with Blockchain-Based Smart Contracts. Academy of Management Review, . Bacharach, S. B. (1989). Organizational Theories: Some Criteria for Evaluation. Academy of Management Review, 14(4), 496-515. Rivard, S. (2021). Theory Building is Neither an Art Nor a Science. It is a Craft. Journal of Information Technology, 36(3), 316-328. Leidner, D. E., & Gregory, R. W. (2024). About Theory and Theorizing. Journal of the Association for Information Systems, 25(3), 501-521.

Josh Pantony is a co-founder and CEO of Boosted.ai – an artificial intelligence company that brings advanced machine learning tools to institutional investors. Since starting Boosted.ai in 2017, the company has helped dozens of investment managers – whose AUM totals over $1 trillion – implement machine learning in their portfolios. Prior to founding Boosted.ai, Josh was a Principal Machine Learning engineer at Bloomberg for 4 years. At Bloomberg, he helped start and build numerous critical Machine Learning initiatives including Ranking, Recommendation, Question and Answering, Crowd Sourcing, and Knowledge Graphs. He also acted as a consultant on numerous initiatives across the company and helped build several ML teams. Listen to this insightful RIA episode with Josh Pantony about how Boosted.ai supercharges productivity in wealth management. Here is what to expect on this week's show: - How Boosted.ai helps investment managers by automating repetitive tasks like portfolio commentary, risk monitoring, and discounted cash flow analysis. How Boosted.ai is a SaaS tool that allows users to log in and build custom automation tailored to their investment management needs. - Why hiring ahead can help organizations to scale quickly as the business grows. - How Boosted.ai saves investment managers between 20% and 50% of their weekly hours by automating tasks. - How Boosted.ai offers insights and analysis on supply chain effects or shareholder meeting changes. Connect with Josh: Links Mentioned: Boosted.ai X @BoostedAi LinkedIn linkedin.com/company/boostedai Learn more about your ad choices. Visit megaphone.fm/adchoices

Many industries, including toy and game makers, are increasingly depending on crowdsourcing for fresh ideas and to help decide what products to make. Outsourcing creativity to the masses can offer valuable feedback… but that data can also be misinterpreted, misunderstood or provide just-plain-bogus ideas. The annual toy business in the United States is a $100 billion-dollar industry. It's by far the biggest toy market in the world, but apparently the “titans of toys” are out of ideas. So now, their attitude about creating new “stuff” has shifted to an attitude of – you know what - you do it!  Really, no really! To breakdown the inner workings of today's toy and game world, Jason and Peter turned to an old friend, David Fuhrer who has created and licensed more than 300 toys, games, and household products, generating retail sales of over $1 billion dollars. These include iconic products such as Nerf Vortex Football, Aqua Doodle, Twisty Petz, Guitar Hero Air Guitar, Bounce Around Tigger, Hershey's S'mores Maker, and more. AND…he's also the Guinness World Record holder for being the fastest backwards talker. Really, no Really. *** IN THIS EPISODE: 24 toy companies rejected one of the best-selling toys! How does an idea become a product, solve an everyday problem or innovate a classic? In 2024 is it possible to successfully pitch a Beanie Baby-like idea? The billion-dollar idea for a Tailor Swift product. The surprising new trends in toys and games. Why physical games and puzzles have made a comeback. How do crowd sourced ideas pay off for the idea creators? How David's “backward talking” launched a career. We challenged David to translate classic Seinfeld lines…backwards. The deadliest toys ever put on the market! Googleheim: Test your toy knowledge! *** FOLLOW DAVID: X: @funanuf123 *** FOLLOW REALLY NO REALLY: www.reallynoreally.com Instagram YouTube TikTok Facebook Threads XSee omnystudio.com/listener for privacy information.

I need help! If you've ever been to OhHappyPlants.shop and couldn't figure out what soil to choose for your plant, and thought “if it was just organized THIS WAY it would be so much easier” - I want your thoughts!!!

Send us a Text Message.Are you tired of the negativity and darkness in Hollywood movies? Wish there were more films that uplift and inspire? There's good news! This episode of The Covenant Eyes Podcast dives into the world of Angel Studios, the company shaking up Hollywood by letting YOU decide what movies get made.Join Karen and Rob as they chat with Jared Geesey, Chief Distribution Officer for Angel Studios. You'll hear about:The innovative "Angel Guild" that lets viewers vote on films and greenlight projects.Their incredible success stories, including the box office hit "Sound of Freedom."How Angel Studios is partnering with theaters to bring wholesome content to the big screen.How YOU can get involved and help change Hollywood for the better!Ready to be a part of the movement? We'll give you all the info you need to join the Angel Guild and start voting on movies that amplify light in the world. Listen now and help us take back Hollywood!_______________________________________ANGEL STUDIOS GUILD:https://www.angel.com/guild/joinANGEL STUDIOS:https://www.angel.com/__________________________________________COVENANT EYES WEBSITE:HTry Covenant Eyes for FREE today!Use Promo Code: FreePodcast

Michelle Cordeiro Grant is an accomplished entrepreneur with a passion for creating innovative, community-based businesses. In 2016, she founded the intimates brand LIVELY and sold it three years later for $105 million. Following this successful exit, she pivoted to her next venture: GORGIE, a wellness energy drink.In this episode of DTC Pod, Michelle shares valuable insights on building a successful DTC brand from the ground up. She discusses the importance of building a community around the brand, leveraging social media, and using data and customer feedback to drive product innovation.Michelle also touches on the strategic decisions behind GORGIE's retail expansion, partnering with influential creators, and creating a magazine to inspire and connect with customers. Throughout the conversation, she emphasizes authenticity, quality, and customer-centricity as crucial elements in establishing a strong brand identity.Interact with other DTC experts and access our monthly fireside chats with industry leaders on DTC Pod Slack.On this episode of DTC Pod, we cover:1. Brand and Community Building2. Data-Driven Product Development3. Nurturing Retail Partnerships4. Strategic Market Positioning5. Defining and Capturing Your ICP6. Organic Influencer Partnerships7. The Intersection of Fashion and CPGTimestamps00:00 Michelle Cordeiro Grant's background; the idea behind GORGIE03:46 How customer feedback factored in product and brand development for GORGIE07:40 Community as a buzzword vs as part of brand identity10:01 Community building as an integral part of brand building11:28 How to keep an online community engaged; consumer vs community funnel16:50 GORGIE's partnerships: Erewhon, Whole Foods, Sprouts, H-E-B, etc.20:05 Overlaps between two industries: fashion and CPG22:14 The story and vision behind GORGIE's magazine23:57 Defining GORGIE's ICP and building a brand around them26:54 GORGIE's growth channels, how they nurture top-of-funnel and retain customers29:42 Year two of GORGIE: online and retail initiatives31:19 Brand-creator partnerships and the importance of organic alignmentShow notes powered by CastmagicPast guests & brands on DTC Pod include Gilt, PopSugar, Glossier, MadeIN, Prose, Bala, P.volve, Ritual, Bite, Oura, Levels, General Mills, Mid Day Squares, Prose, Arrae, Olipop, Ghia, Rosaluna, Form, Uncle Studios & many more.  Additional episodes you might like:• #175 Ariel Vaisbort - How OLIPOP Runs Influencer, Community, & Affiliate Growth• #184 Jake Karls, Midday Squares - Turning Your Brand Into The Influencer With Content• #205 Kasey Stewart: Suckerz- - Powering Your Launch With 300 Million Organic Views• #219 JT Barnett: The TikTok Masterclass For Brands• #223 Lauren Kleinman: The PR & Affiliate Marketing Playbook• ​​​​#243 Kian Golzari - Source & Develop Products Like The World's Best Brands-----Have any questions about the show or topics you'd like us to explore further?Shoot us a DM; we'd love to hear from you.Want the weekly TL;DR of tips delivered to your mailbox?Check out our newsletter here.Projects the DTC Pod team is working on:DTCetc - all our favorite brands on the internetOlivea - the extra virgin olive oil & hydroxytyrosol supplementCastmagic - AI Workspace for ContentFollow us for content, clips, giveaways, & updates!DTCPod InstagramDTCPod TwitterDTCPod TikTok  Michelle Cordeiro Grant - Founder and CEO of GORGIEBlaine Bolus - Co-Founder of CastmagicRamon Berrios - Co-Founder of Castmagic

For more from Doug, subscribe to Canon+: https://mycanonplus.com/

Every Tuesday PatGPT, Patrick J. McGinnis the host of FOMO Sapiens generates responses to key questions in entrepreneurship, business, productivity, and life. Learn more about your ad choices. Visit megaphone.fm/adchoices

Send Everyday AI and Jordan a text messageWin a free year of ChatGPT or other prizes! Find out out.Yeah, AI is cool. But have you tried AI WITH good data?! If you're running into AI implementation bottlenecks, it could be your data to blame. Matthijs de Vries, Founder & CEO of Nuklai, joins us to tackle AI and data.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Matthijs questions on AI and dataRelated Episodes: Ep 268: AI's Data-Driven Decision ParadoxEp 145: NVIDIA Leader Talks GenAI + Data: Unlocking new ways to interact with our worldUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Data and Large Language Models (LLMs)2. Practical Data Strategies3. Data Quality IssuesTimestamps:01:35 Daily AI news05:00 About Matthijs and Nuklai06:48 Data bottleneck hinders implementation of generative AI.10:26 Start with a goal, leverage data effectively.13:20 Collaborating on data is costly, causing limitations.15:46 Standardize data access to improve overall efficiency.18:46 Discussion on the use of synthetic data.23:13 Challenges for small AI projects due to funding.27:33 Crowdsourcing data important for future developments.28:38 Data used to improve bread quality. Multiple purposes.Keywords:Everyday AI, Jordan Wilson, generative AI, data bottleneck, OpenAI, GPT 4, SAM 2, video segmentation, Meta, AI Studio, chatbot creation, llama 3.1 model, Matt deFries, Nuclei, structured data, unstructured data, Large Language Models (LLMs), AI implementation, data in silos, data consortiums, data pipelines, data collection, memory efficiency, synthetic data, crowdsourcing data, data quality, human-generated data, collaboration, data science, philosophy in data. Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

Have you contributed to someone's GoFundMe? Have you helped a friend with a Kickstarter Campaign? What lessons can you learn from Skeletor? In this week's episode Shawn & George talk about Crowdsourcing or Crowdfunding. What are some things you can do to help, and what are some things you should avoid?  What are the various platforms available - although there are likely TONS more out there than what we cover in today's episode, including: GoFundMe Patreon (our favorite - https://www.patreon.com/crushingDebt) Raise Right Kickstarter Let us know if you enjoy this episode and, if so, please share it with your friends! Please also visit our sponsors: Magic Mind - https://www.magicmind.com/CRUSHING20. It keeps us focused and energized all day with no crash and no inability to get to sleep! Sam Cohen of Attorneys First Insurance for Attorneys and Title Companies looking to get a quote on Errors & Ommissions (malpractice) Insurance coverage. www.AttorneysFirst.com.   To contact George Curbelo, you can email him at GCFinancialCoach21@gmail.com or follow his Tiktok channel - https://www.tiktok.com/@curbelofinancialcoach   To contact Shawn Yesner, you can email him at Shawn@Yesnerlaw.com or visit www.YesnerLaw.com. And please consider a donation to Pancreatic Cancer research and education by joining Shawn's team at MY Legacy Striders: http://support.pancan.org/goto/MYLegacyStriders08  

Some birds are famous for being extinct, like the Dodo and the passenger pigeon.But how do we prevent species from reaching that point? One of the starting points is to try and track down the birds that are “lost to science.” These are birds that have not been documented in over a decade, but just might still be out there, if we look for them.A new study analyzed data, images, and recordings from platforms that crowdsource observations from all over the world to identify birds “lost to science.” In total, the project, called The Search for Lost Birds identified 126 such species.SciFri producer Kathleen Davis is joined by Dr. John Mittermeier, director of the Search for Lost Birds at the American Bird Conservancy to talk more about the findings of this research and what it's like to track down a “lost” bird.Transcripts for each segment will be available after the show airs on sciencefriday.com. Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.

In this episode of the Inspired Money Live Stream Podcast, we navigate the world of classic car restoration and collecting. Our expert panel includes Bruce Meyer, Ed Bolian, Lauren Fix, and Phillip Griot. Each brings a wealth of experience to discuss the intricacies of restoring and collecting classic cars. Navigating the Path to Successful Car Restoration Classic car restoration is a blend of art and science, requiring both passion and precision. Our distinguished guests share their expertise, offering valuable insights into the world of car restoration and collecting. From understanding market trends to choosing the right restoration shop, this episode covers all essential aspects.

Plowsy's penultimate shows. Doug has a problem with the word penultimate. Tee times for the FPCC. Weather forecast for Sunday. Tim has to play quickly. Local rules for the FPCC. Iggy wants people to have a rule book. Cart path only. Little league baseball. Article about Cardinal attendance. Some reasons why people aren't going down to Busch. Ways to watch sports nowadays. Crowdsourcing season. Tim's Mother-in-law wishes Plowsy good luck. Spring training memories. Butterfly effect. Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.